[AI]NEPC: AI sprint risks environmental catastrophe

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The government is urged to mandate stricter reporting for data centres to mitigate environmental risks associated with the AI sprint.

A report published today by the

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(NEPC) highlights the urgent need for data centres to adopt greener practices, particularly as the government’s AI Opportunities Action Plan gains traction.

The report, Engineering Responsible AI: Foundations for Environmentally Sustainable AI, was developed in collaboration with the Royal Academy of Engineering, the Institution of Engineering and Technology, and BCS, the Chartered Institute of IT.

While stressing that data centres enabling AI systems can be built to consume fewer resources like energy and water, the report highlights that infrastructure and regulatory conditions must align for these efficiencies to materialise.

Unlocking the potential of AI while minimising environmental risks  

AI is heralded as capable of driving economic growth, creating jobs, and improving livelihoods. Launched as a central pillar of the ***’s tech strategy, the

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is intended to “boost economic growth, provide jobs for the future and improve people’s everyday lives.”  

Use cases for AI that are already generating public benefits include

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, forecasting weather events, optimising energy systems, and even

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and

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efforts. However, this growing reliance on AI also poses environmental risks from the infrastructure required to power these systems.  

Data centres, which serve as the foundation of AI technologies, consume vast amounts of energy and water. Increasing demand has raised concerns about global competition for limited resources, such as sustainable energy and drinking water.

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, for instance, have recorded rising water usage by their data centres each year since 2020. Much of this water comes from drinking sources, sparking fears about resource depletion.  

With plans already in place to reform the ***’s planning system to facilitate the construction of data centres, the report calls for urgent policies to manage their environmental impact. Accurate and transparent data on resource consumption is currently lacking, which

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to assess the true scale of these impacts and act accordingly.

Five steps to sustainable AI  

The NEPC is urging the government to spearhead change by prioritising sustainable AI development. The report outlines five key steps policymakers can act upon immediately to position the *** as a leader in resource-efficient AI:  

1. Expand environmental reporting mandates
2. Communicate the sector’s environmental impacts
3. Set sustainability requirements for data centres
4. Reconsider data collection, storage, and management practices
5. Lead by example with government investment

Mandatory environmental reporting forms a cornerstone of the recommendations. This involves measuring data centres’ energy sources, water consumption, carbon emissions, and e-waste recycling practices to provide the resource use data necessary for policymaking.  

Raising public awareness is also vital. Communicating the environmental costs of AI can encourage developers to optimise AI tools, use smaller datasets, and adopt

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approaches. Notably, the report recommends embedding environmental design and sustainability topics into computer science and AI education at both school and university levels.  

Smarter, greener data centres  

One of the most urgent calls to action involves redesigning data centres to reduce their environmental footprint. The report advocates for innovations like waste heat recovery systems, zero drinking water use for cooling, and the exclusive use of 100% carbon-free energy certificates.  

Efforts like those at Queen Mary University of London, where residual heat from a campus data centre is repurposed to provide heating and hot water, offer a glimpse into the possibilities of greener tech infrastructure.  

In addition, the report suggests revising

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on mandatory data retention to reduce the unnecessary environmental costs of storing vast amounts of data long-term. Proposals for a National Data Library could drive best practices by centralising and streamlining data storage.  

Professor Tom Rodden, Pro-Vice-Chancellor at the University of Nottingham and Chair of the working group behind the report, urged swift action:  

“In recent years, advances in AI systems and services have largely been driven by a race for size and scale, demanding increasing amounts of computational power. As a result, AI systems and services are growing at a rate unparalleled by other high-energy systems—generally without much regard for resource efficiency.  

“This is a dangerous trend, and we face a real risk that our development, deployment, and use of AI could do irreparable damage to the environment.”  

Rodden added that reliable data on these impacts is critical. “To build systems and services that effectively use resources, we first need to effectively monitor their environmental cost. Once we have access to trustworthy data… we can begin to effectively target efficiency in development, deployment, and use – and plan a sustainable AI future for the ***.”

Dame Dawn Childs, CEO of Pure Data Centres Group, underscored the role of engineering in improving efficiency. “Some of this will come from improvements to AI models and hardware, making them less energy-intensive. But we must also ensure that the data centres housing AI’s computing power and storage are as sustainable as possible.  

“That means prioritising renewable energy, minimising water use, and reducing carbon emissions – both directly and indirectly. Using low-carbon building materials is also essential.”  

Childs emphasised the importance of a coordinated approach from the start of projects. “As the *** government accelerates AI adoption – through AI Growth Zones and streamlined planning for data centres – sustainability must be a priority at every step.”  

For Alex Bardell, Chair of BCS’ Green IT Specialist Group, the focus is on optimising AI processes. “Our report has discussed optimising models for efficiency. Previous attempts to limit the drive toward increased computational power and larger models have faced significant resistance, with concerns that the *** may fall behind in the AI arena; this may not necessarily be true.  

“It is crucial to reevaluate our approach to developing sustainable AI in the future.”  

Time for transparency around AI environmental risks

Public awareness of AI’s environmental toll remains low. Recent research by the Institution of Engineering and Technology (IET) found that fewer than one in six *** residents are aware of the significant environmental costs associated with AI systems.  

“AI providers must be transparent about these effects,” said Professor Sarvapali Ramchurn, CEO of Responsible AI *** and a Fellow of the IET. “If we cannot measure it, we cannot manage it, nor ensure benefits for all. This report’s recommendations will aid national discussions on the sustainability of AI systems and the trade-offs involved.”  

As the *** pushes forward with ambitious plans to lead in AI development, ensuring environmental sustainability must take centre stage. By adopting policies and practices outlined in the NEPC report, the government can support AI growth while safeguarding finite resources for future generations.

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